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                <div id="site-name"><a href="../../sphinx_pages/build/html/index.html">Shark machine learning library</a></div>
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<h3><a id="index_r" name="index_r"></a>- r -</h3><ul>
<li>R2&#160;:&#160;<a class="el" href="structshark_1_1_radius_margin_quotient_1_1_result.html#a652933073158d9486c6208d2151b7a0a">shark::RadiusMarginQuotient&lt; InputType, CacheType &gt;::Result</a></li>
<li>RadiusMarginQuotient()&#160;:&#160;<a class="el" href="classshark_1_1_radius_margin_quotient.html#aca26f45e233b839060e16d49bf27d72b">shark::RadiusMarginQuotient&lt; InputType, CacheType &gt;</a></li>
<li>RANDOM&#160;:&#160;<a class="el" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ab2bbffc9336fbe61c3b667f8f3f0672eaa780ebd48025014f013b4fa68cc4ae45">shark::KernelBudgetedSGDTrainer&lt; InputType, CacheType &gt;</a>, <a class="el" href="classshark_1_1_remove_budget_maintenance_strategy.html#afa4ea66adb4e0114f913bf49e9737804a33a06748f85038c3e3652ffc18ec07fe">shark::RemoveBudgetMaintenanceStrategy&lt; InputType &gt;</a></li>
<li>randomType&#160;:&#160;<a class="el" href="classshark_1_1_r_b_m.html#a5b791282749a918b7894a9cb01c29a36">shark::RBM&lt; VisibleLayerT, HiddenLayerT, randomT &gt;</a></li>
<li>rank()&#160;:&#160;<a class="el" href="classshark_1_1_individual.html#aa4a1bb36f0ccbdc08f68ae85a198d713">shark::Individual&lt; PointType, FitnessTypeT, Chromosome &gt;</a></li>
<li>RankingSvmTrainer()&#160;:&#160;<a class="el" href="classshark_1_1_ranking_svm_trainer.html#a54df15bbf0fc10b90891b7e47243657a">shark::RankingSvmTrainer&lt; InputType, CacheType &gt;</a></li>
<li>rankOneUpdate()&#160;:&#160;<a class="el" href="classshark_1_1_multi_variate_normal_distribution_cholesky.html#ab0afb717b2e773692a4b972c104708d1">shark::MultiVariateNormalDistributionCholesky</a></li>
<li>RBFLayer()&#160;:&#160;<a class="el" href="classshark_1_1_r_b_f_layer.html#aa6b23dfb2c34ecbb5d96034eb17bd5b6">shark::RBFLayer</a></li>
<li>RBM&#160;:&#160;<a class="el" href="classshark_1_1_contrastive_divergence.html#a25fb98f539ea0bf360835711d7608402">shark::ContrastiveDivergence&lt; Operator &gt;</a>, <a class="el" href="classshark_1_1_energy_storing_tempered_markov_chain.html#aec1460bd58ab50bfd1a0ab043810936f">shark::EnergyStoringTemperedMarkovChain&lt; Operator &gt;</a>, <a class="el" href="classshark_1_1_exact_gradient.html#a782d614ec71be0e27fb7fba3bfaa58f4">shark::ExactGradient&lt; RBMType &gt;</a></li>
<li>rbm()&#160;:&#160;<a class="el" href="classshark_1_1_gibbs_operator.html#a0f5eaa3488fc9152a26be8c4a9c11c41">shark::GibbsOperator&lt; RBMType &gt;</a></li>
<li>RBM&#160;:&#160;<a class="el" href="classshark_1_1_gibbs_operator.html#a9de44a00cd05bc1736b0503d25af6bd4">shark::GibbsOperator&lt; RBMType &gt;</a>, <a class="el" href="classshark_1_1_markov_chain.html#adf255589fa5c7f8a2e0f868b4987efec">shark::MarkovChain&lt; Operator &gt;</a>, <a class="el" href="classshark_1_1_multi_chain_approximator.html#ab7ff1b4d01f6a2568f15cd9201bd6469">shark::MultiChainApproximator&lt; MarkovChainType &gt;</a>, <a class="el" href="classshark_1_1_r_b_m.html#ab7e7691d5840fe1a87b9b220e9d9ab47">shark::RBM&lt; VisibleLayerT, HiddenLayerT, randomT &gt;</a>, <a class="el" href="classshark_1_1_single_chain_approximator.html#af212889ee84951451d6166be19f0fb11">shark::SingleChainApproximator&lt; MarkovChainType &gt;</a>, <a class="el" href="classshark_1_1_tempered_markov_chain.html#ac3f138b5f03d858ed5c034bb0016db1c">shark::TemperedMarkovChain&lt; Operator &gt;</a></li>
<li>read()&#160;:&#160;<a class="el" href="classshark_1_1_abstract_clustering.html#a716046b5e610f0a26e3462d8dda27f45">shark::AbstractClustering&lt; InputT &gt;</a>, <a class="el" href="classshark_1_1_abstract_line_search_optimizer.html#aac2d86149b2232e949f41f4d04f86002">shark::AbstractLineSearchOptimizer&lt; SearchPointType &gt;</a>, <a class="el" href="classshark_1_1_abstract_metric.html#a8286ec6f54f35ab53a92d42cb251d6e4">shark::AbstractMetric&lt; InputTypeT &gt;</a>, <a class="el" href="classshark_1_1_abstract_model.html#a11203dd6f50218e4c341a5d24ff5d543">shark::AbstractModel&lt; InputTypeT, OutputTypeT, ParameterVectorType &gt;</a>, <a class="el" href="classshark_1_1_adam.html#ab0560f21fb8e543bd040c37e3ba3a35e">shark::Adam&lt; SearchPointType &gt;</a>, <a class="el" href="classshark_1_1_a_r_d_kernel_unconstrained.html#ab07f20b41ea7c29f6b0297ba34cf040d">shark::ARDKernelUnconstrained&lt; InputType &gt;</a>, <a class="el" href="classshark_1_1_b_f_g_s.html#a1f423945c791d56e6ede294fce241531">shark::BFGS&lt; SearchPointType &gt;</a>, <a class="el" href="classshark_1_1_binary_layer.html#a1c4afed5e0ebfb89c536183123c5360f">shark::BinaryLayer</a>, <a class="el" href="classshark_1_1_bipolar_layer.html#a978f4c7acbf8905f6ceaa60f8db7c057">shark::BipolarLayer</a>, <a class="el" href="classshark_1_1_c_a_r_tree.html#aa9417860a291a1f8703c89cb0268c29c">shark::CARTree&lt; LabelType &gt;</a>, <a class="el" href="classshark_1_1_centroids.html#aac250dde37c2e65581d3f9714891e04d">shark::Centroids</a>, <a class="el" href="classshark_1_1_c_g.html#a3a13261a94421b23929ff9290e9f92e2">shark::CG&lt; SearchPointType &gt;</a>, <a class="el" href="classshark_1_1_classifier.html#acb5a2d1e5b06c0ef549b3e55d495cdc7">shark::Classifier&lt; Model &gt;</a>, <a class="el" href="classshark_1_1_clustering_model.html#a9992375d324ad22ea888e0ecd4c9303f">shark::ClusteringModel&lt; InputT, OutputT &gt;</a>, <a class="el" href="classshark_1_1_c_m_a.html#af043e55b471f98e30cf86e00349ddb5b">shark::CMA</a>, <a class="el" href="classshark_1_1_c_m_a_c_map.html#a1af8ce06b347c7ab4c4069cd196d85dd">shark::CMACMap</a>, <a class="el" href="classshark_1_1_c_m_s_a.html#ae566583eb68bd89e3c728d117fea8c3a">shark::CMSA</a>, <a class="el" href="classshark_1_1_concatenated_model.html#a7106a8909f995781d3a426fd5ff85518">shark::ConcatenatedModel&lt; VectorType &gt;</a>, <a class="el" href="classshark_1_1_conv2_d_model.html#ae6bb09a47a944952e12a735fc1be7d43">shark::Conv2DModel&lt; VectorType, ActivationFunction &gt;</a>, <a class="el" href="classshark_1_1_cross_entropy_method.html#a51ac45a57be33ee94e76fc7024870628">shark::CrossEntropyMethod</a>, <a class="el" href="classshark_1_1_c_svm_derivative.html#a4a57799c4b3da25509930d480a0aed19">shark::CSvmDerivative&lt; InputType, CacheType &gt;</a>, <a class="el" href="group__shark__globals.html#gade09075aa5a3e014a4204d5589f0012c">shark::Data&lt; Type &gt;</a>, <a class="el" href="classshark_1_1_discrete_kernel.html#af1eb1494ee1d205dab4f4b276f332de6">shark::DiscreteKernel</a>, <a class="el" href="classshark_1_1_dropout_layer.html#a437851bcf235d76b8b9a5420f642bdea">shark::DropoutLayer&lt; VectorType &gt;</a>, <a class="el" href="classshark_1_1_elitist_c_m_a.html#a1c7671e276787d93cc32ff6165d4b5f6">shark::ElitistCMA</a>, <a class="el" href="classshark_1_1_gaussian_layer.html#a8c717e6c37de891dd44a1a4640d589a0">shark::GaussianLayer</a>, <a class="el" href="classshark_1_1_gaussian_rbf_kernel.html#a08353ce4a1575e9dc100e25a71b8643f">shark::GaussianRbfKernel&lt; InputType &gt;</a>, <a class="el" href="classshark_1_1_gaussian_task_kernel.html#ac89541fe765cdc20608138848b66ac59">shark::GaussianTaskKernel&lt; InputTypeT &gt;</a>, <a class="el" href="classshark_1_1_grid_search.html#a8fa6193d9d0509929c827c59b62e3b3a">shark::GridSearch</a>, <a class="el" href="classshark_1_1_indicator_based_m_o_c_m_a.html#a5fe33e996dc9d626c2067fc02425297b">shark::IndicatorBasedMOCMA&lt; Indicator &gt;</a>, <a class="el" href="classshark_1_1_indicator_based_real_coded_n_s_g_a_i_i.html#ad65c4da2221a12a9a8598c4cda6b6700">shark::IndicatorBasedRealCodedNSGAII&lt; Indicator &gt;</a>, <a class="el" href="classshark_1_1_indicator_based_steady_state_m_o_c_m_a.html#afa45b2fca7a2d0097533013ae47bed86">shark::IndicatorBasedSteadyStateMOCMA&lt; Indicator &gt;</a>, <a class="el" href="classshark_1_1_i_serializable.html#ad4ad9a7c274deff642f91e98417fbc63">shark::ISerializable</a>, <a class="el" href="classshark_1_1_kernel_expansion.html#a3b4d90a82af13e81f0ddb9bbc133a2a8">shark::KernelExpansion&lt; InputType &gt;</a>, <a class="el" href="group__shark__globals.html#ga846df24c10a85b0454cf06cabe55dcc2">shark::LabeledData&lt; InputT, LabelT &gt;</a>, <a class="el" href="classshark_1_1_l_b_f_g_s.html#adbf39210c8d5255385700e2f5fb6e019">shark::LBFGS&lt; SearchPointType &gt;</a>, <a class="el" href="classshark_1_1_linear_kernel.html#a458cbc276f274f6892a3ba2190510d6d">shark::LinearKernel&lt; InputType &gt;</a>, <a class="el" href="classshark_1_1_linear_model.html#add90eac4cd04eb844acbf0ab0b32a9af">shark::LinearModel&lt; InputType, ActivationFunction &gt;</a>, <a class="el" href="classshark_1_1_line_search.html#aaff584e0cb9bb7fd7eddb6f7420042b8">shark::LineSearch&lt; SearchPointType &gt;</a>, <a class="el" href="classshark_1_1_model_kernel.html#ad54f86fc8cc3e01d9be6f4e2e7a5f3ac">shark::ModelKernel&lt; InputType &gt;</a>, <a class="el" href="classshark_1_1_monomial_kernel.html#a098d0c0241648053542bc4ef4dd77acf">shark::MonomialKernel&lt; InputType &gt;</a>, <a class="el" href="structshark_1_1_multi_task_sample.html#a5de304c0f2e7d2cfecd56dc305264559">shark::MultiTaskSample&lt; InputTypeT &gt;</a>, <a class="el" href="classshark_1_1_nested_grid_search.html#acc163d14a00c8801b118dc6e48f1bb44">shark::NestedGridSearch</a>, <a class="el" href="classshark_1_1_neuron_layer.html#abee464995a2511f6433aea9e3e51cb44">shark::NeuronLayer&lt; NeuronType, VectorType &gt;</a>, <a class="el" href="classshark_1_1_normalizer.html#a70516fb4229bc2e1ac915e69d091841c">shark::Normalizer&lt; VectorType &gt;</a>, <a class="el" href="classshark_1_1_one_versus_one_classifier.html#ad4eedddd8b965056551409b8525c427e">shark::OneVersusOneClassifier&lt; InputType, VectorType &gt;</a>, <a class="el" href="classshark_1_1_optimization_trainer.html#ad449532e71b350608c5893be63d1b3d8">shark::OptimizationTrainer&lt; Model, LabelTypeT &gt;</a>, <a class="el" href="classshark_1_1_point_search.html#afc65c684eb51d11d522c324ea6ebce6a">shark::PointSearch</a>, <a class="el" href="classshark_1_1_polynomial_kernel.html#aa672f4e0ffee8eded6e34d7c7f081636">shark::PolynomialKernel&lt; InputType &gt;</a>, <a class="el" href="classshark_1_1_pooling_layer.html#a7844e7de771788a2cd7b8811b45f98b8">shark::PoolingLayer&lt; VectorType &gt;</a>, <a class="el" href="classshark_1_1_product_kernel.html#aeac1d8abd81b80d1550213f5a0cdd0d0">shark::ProductKernel&lt; InputType &gt;</a>, <a class="el" href="classshark_1_1_r_b_f_layer.html#a332b99e89c51c3a80da79691f7b878f5">shark::RBFLayer</a>, <a class="el" href="classshark_1_1_r_b_m.html#a1f347deac6a9d06e1ae485dfa0fd276e">shark::RBM&lt; VisibleLayerT, HiddenLayerT, randomT &gt;</a>, <a class="el" href="classshark_1_1_resize_layer.html#a11d506d07029226e1436c78e3ee75df7">shark::ResizeLayer&lt; VectorType &gt;</a>, <a class="el" href="classshark_1_1_rprop.html#ac754e595049b6dbbc114ac9c0f54ce0b">shark::Rprop&lt; SearchPointType &gt;</a>, <a class="el" href="classshark_1_1_scaled_kernel.html#a7f6e20a47ab00f460f22ade7bb4d9d47">shark::ScaledKernel&lt; InputType &gt;</a>, <a class="el" href="classshark_1_1_simplex_downhill.html#ae4c829c04db9534c9c44e3f893a512b5">shark::SimplexDownhill</a>, <a class="el" href="classshark_1_1_s_m_s_e_m_o_a.html#a1dc7fa02907634dc5d2a7d01f99cc431">shark::SMSEMOA</a>, <a class="el" href="classshark_1_1_steepest_descent.html#aedba477b932e74a2228e40403ca72664">shark::SteepestDescent&lt; SearchPointType &gt;</a>, <a class="el" href="classshark_1_1_typed_flags.html#a93b8873116122847eddc92a3f975cc06">shark::TypedFlags&lt; Flag &gt;</a>, <a class="el" href="classshark_1_1_weighted_sum_kernel.html#a03eb586f4658acdf41643b761f932b3d">shark::WeightedSumKernel&lt; InputType &gt;</a></li>
<li>RealCodedNSGAIII()&#160;:&#160;<a class="el" href="classshark_1_1_real_coded_n_s_g_a_i_i_i.html#a02a9bb202121e316d486b15b24b8856b">shark::RealCodedNSGAIII</a></li>
<li>recombinationType()&#160;:&#160;<a class="el" href="classshark_1_1_c_m_a.html#a013cafb7bc9296b5a07e8f400be1d538">shark::CMA</a></li>
<li>RecombinationType&#160;:&#160;<a class="el" href="classshark_1_1_c_m_a.html#aafbd8e245dd9f8aad0e0e597557c9eb3">shark::CMA</a></li>
<li>reduceBudget()&#160;:&#160;<a class="el" href="classshark_1_1_merge_budget_maintenance_strategy_3_01_real_vector_01_4.html#a50f9819caf20e6bedad5e6735e5db2e0">shark::MergeBudgetMaintenanceStrategy&lt; RealVector &gt;</a></li>
<li>reference&#160;:&#160;<a class="el" href="structshark_1_1_batch_3_01shark_1_1blas_1_1compressed__vector_3_01_t_01_4_01_4.html#ab069b3c0404e1bd4b788bca5499d1d75">shark::Batch&lt; shark::blas::compressed_vector&lt; T &gt; &gt;</a>, <a class="el" href="classshark_1_1_data_view.html#ae4aed6ddb082c41f4db8025b202ed236">shark::DataView&lt; DatasetType &gt;</a>, <a class="el" href="classshark_1_1_energy_storing_tempered_markov_chain.html#acb70640b149f2347d316fdf492db8a55">shark::EnergyStoringTemperedMarkovChain&lt; Operator &gt;</a>, <a class="el" href="classshark_1_1_markov_chain.html#aefa956dcbecb71821ee9ab0f2b52b36d">shark::MarkovChain&lt; Operator &gt;</a>, <a class="el" href="classshark_1_1_tempered_markov_chain.html#af1d86eb103bbb09c1f58684757baae85">shark::TemperedMarkovChain&lt; Operator &gt;</a>, <a class="el" href="structshark_1_1_weighted_data_batch.html#aab93ae72cede17722ce1d1e831e5186d">shark::WeightedDataBatch&lt; DataBatchType, WeightBatchType &gt;</a></li>
<li>referenceVectors()&#160;:&#160;<a class="el" href="classshark_1_1_r_v_e_a.html#ac8650000c2627c4324370f222ee454eb">shark::RVEA</a></li>
<li>regularization()&#160;:&#160;<a class="el" href="classshark_1_1_l_d_a.html#a57673aefbc9282a1772ee99121a09632">shark::LDA</a>, <a class="el" href="classshark_1_1_linear_regression.html#a178a2c8fc36f020fd353256fa8d044ab">shark::LinearRegression</a></li>
<li>RegularizationNetworkTrainer()&#160;:&#160;<a class="el" href="classshark_1_1_regularization_network_trainer.html#a9893276bab3d8102d1f1d8610e7f120c">shark::RegularizationNetworkTrainer&lt; InputType &gt;</a></li>
<li>regularizationParameters()&#160;:&#160;<a class="el" href="classshark_1_1_abstract_svm_trainer.html#ad06550eb45e46ff02e4789ea1b916c75">shark::AbstractSvmTrainer&lt; InputType, LabelType, Model, Trainer &gt;</a></li>
<li>RegularizedKernelMatrix()&#160;:&#160;<a class="el" href="classshark_1_1_regularized_kernel_matrix.html#a36bdf57606893668b6b7fc2e187d38fc">shark::RegularizedKernelMatrix&lt; InputType, CacheType &gt;</a></li>
<li>RemoveBudgetMaintenanceStrategy()&#160;:&#160;<a class="el" href="classshark_1_1_remove_budget_maintenance_strategy.html#a381bddc222d4060c66065b7afa16247b">shark::RemoveBudgetMaintenanceStrategy&lt; InputType &gt;</a></li>
<li>RemoveStrategyFlavor&#160;:&#160;<a class="el" href="classshark_1_1_remove_budget_maintenance_strategy.html#afa4ea66adb4e0114f913bf49e9737804">shark::RemoveBudgetMaintenanceStrategy&lt; InputType &gt;</a></li>
<li>reorderBFS()&#160;:&#160;<a class="el" href="classshark_1_1_c_a_r_tree.html#ae38eaeb8b77503cb46cdefdce5d363ca">shark::CARTree&lt; LabelType &gt;</a></li>
<li>reorderElements()&#160;:&#160;<a class="el" href="group__shark__globals.html#ga42c8e836dbb5860d4044d8b19732f794">shark::Data&lt; Type &gt;</a>, <a class="el" href="group__shark__globals.html#gae98f8421736e774da09ea3f15d985cfe">shark::LabeledData&lt; InputT, LabelT &gt;</a></li>
<li>repartition()&#160;:&#160;<a class="el" href="group__shark__globals.html#ga43280de21c8ba42f381a555cd8f367fe">shark::Data&lt; Type &gt;</a>, <a class="el" href="group__shark__globals.html#ga298a81625c3bcd482c3b68daf815c70b">shark::LabeledData&lt; InputT, LabelT &gt;</a></li>
<li>REQUIRES_CLOSEST_FEASIBLE&#160;:&#160;<a class="el" href="classshark_1_1_abstract_optimizer.html#a77bf437afee3445601c680cc652410f0addbe7762d6f4f8769cc06f61bc9c5c28">shark::AbstractOptimizer&lt; PointType, ResultT, SolutionTypeT &gt;</a></li>
<li>REQUIRES_FIRST_DERIVATIVE&#160;:&#160;<a class="el" href="classshark_1_1_abstract_optimizer.html#a77bf437afee3445601c680cc652410f0ad9b925369d4f923c63792bf455eac1d7">shark::AbstractOptimizer&lt; PointType, ResultT, SolutionTypeT &gt;</a></li>
<li>REQUIRES_SECOND_DERIVATIVE&#160;:&#160;<a class="el" href="classshark_1_1_abstract_optimizer.html#a77bf437afee3445601c680cc652410f0a0a32670987bb219aaf2f6defc70e6f03">shark::AbstractOptimizer&lt; PointType, ResultT, SolutionTypeT &gt;</a></li>
<li>REQUIRES_VALUE&#160;:&#160;<a class="el" href="classshark_1_1_abstract_optimizer.html#a77bf437afee3445601c680cc652410f0af46b9e1111a0858df3670fe12e4ffbf0">shark::AbstractOptimizer&lt; PointType, ResultT, SolutionTypeT &gt;</a></li>
<li>requiresClosestFeasible()&#160;:&#160;<a class="el" href="classshark_1_1_abstract_optimizer.html#aa9332ad1b90deed11ee6709a92964bc9">shark::AbstractOptimizer&lt; PointType, ResultT, SolutionTypeT &gt;</a></li>
<li>requiresFirstDerivative()&#160;:&#160;<a class="el" href="classshark_1_1_abstract_optimizer.html#a13dff098bcde14529b64be653d73d57f">shark::AbstractOptimizer&lt; PointType, ResultT, SolutionTypeT &gt;</a></li>
<li>requiresSecondDerivative()&#160;:&#160;<a class="el" href="classshark_1_1_abstract_optimizer.html#a9da146985b7738554e09f75a670f8c97">shark::AbstractOptimizer&lt; PointType, ResultT, SolutionTypeT &gt;</a></li>
<li>requiresValue()&#160;:&#160;<a class="el" href="classshark_1_1_abstract_optimizer.html#a8645fb354408c89d3537aa87aed49b79">shark::AbstractOptimizer&lt; PointType, ResultT, SolutionTypeT &gt;</a></li>
<li>reset()&#160;:&#160;<a class="el" href="classshark_1_1_abstract_stopping_criterion.html#a2cd08d7ec1a7627ad17ce451b8ba915c">shark::AbstractStoppingCriterion&lt; ResultSetT &gt;</a>, <a class="el" href="classshark_1_1_generalization_loss.html#a986240304b8229257327128e5a35bc61">shark::GeneralizationLoss&lt; PointType &gt;</a>, <a class="el" href="classshark_1_1_generalization_quotient.html#a9b4a09253f251d327ad61a9667a67582">shark::GeneralizationQuotient&lt; PointType &gt;</a>, <a class="el" href="classshark_1_1_h_m_g_selection_criterion.html#af4b4e8aa1266e0e9f5a7a33f1bd5caf3">shark::HMGSelectionCriterion</a>, <a class="el" href="structshark_1_1_lib_s_v_m_selection_criterion.html#a3ea14cbcfedf57f2cd5a630f84a65b11">shark::LibSVMSelectionCriterion</a>, <a class="el" href="structshark_1_1_maximum_gain_criterion.html#addb17e0779dd259a2ce4025fc93ba983">shark::MaximumGainCriterion</a>, <a class="el" href="structshark_1_1_maximum_gradient_criterion.html#a0dfe50ec011b9edf924aa8970e91ef7c">shark::MaximumGradientCriterion</a>, <a class="el" href="classshark_1_1_max_iterations.html#ad4a33c4632a28025c848ebf0c05b63b5">shark::MaxIterations&lt; ResultSet &gt;</a>, <a class="el" href="structshark_1_1_m_v_p_selection_criterion.html#a2c9b635d190d337a2d3aaf454a77cb9d">shark::MVPSelectionCriterion</a>, <a class="el" href="structshark_1_1_qp_mc_box_decomp_1_1_prefered_selection_strategy.html#aaa0b8a9e4cfc1cc1bee321427a0d70b6">shark::QpMcBoxDecomp&lt; Matrix &gt;::PreferedSelectionStrategy</a>, <a class="el" href="structshark_1_1_qp_mc_simplex_decomp_1_1_prefered_selection_strategy.html#af129df59061923a301d6b6a15e985504">shark::QpMcSimplexDecomp&lt; Matrix &gt;::PreferedSelectionStrategy</a>, <a class="el" href="classshark_1_1_training_error.html#a72f7fae3c19205d88603b085c83a2acb">shark::TrainingError&lt; PointType &gt;</a>, <a class="el" href="classshark_1_1_training_progress.html#a6244cca3d61f3d5ce1dee922ed9415ff">shark::TrainingProgress&lt; PointType &gt;</a>, <a class="el" href="classshark_1_1_typed_flags.html#a68f0c572adf112b680ef11531aa9ffb8">shark::TypedFlags&lt; Flag &gt;</a>, <a class="el" href="classshark_1_1_validated_stopping_criterion.html#ad0982fa304282a6ba5d6f8cd48cf2ef8">shark::ValidatedStoppingCriterion</a>, <a class="el" href="structshark_1_1_w_s2_maximum_gradient_criterion.html#aeaad8c0631f5fdbb14fccf22027c56a3">shark::WS2MaximumGradientCriterion</a></li>
<li>resetAccessCount()&#160;:&#160;<a class="el" href="classshark_1_1_example_modified_kernel_matrix.html#a2a54aaf7381259d7541efe5cdff2cc38">shark::ExampleModifiedKernelMatrix&lt; InputType, CacheType &gt;</a>, <a class="el" href="classshark_1_1_gaussian_kernel_matrix.html#ad88e08e23144f2ccf148f35a5b357cfc">shark::GaussianKernelMatrix&lt; T, CacheType &gt;</a>, <a class="el" href="classshark_1_1_kernel_matrix.html#ad76f1daf43dd80689ad630cab48a111a">shark::KernelMatrix&lt; InputType, CacheType &gt;</a>, <a class="el" href="classshark_1_1_modified_kernel_matrix.html#abfbd7330e1e5bb8c783f82adcf91d715">shark::ModifiedKernelMatrix&lt; InputType, CacheType &gt;</a>, <a class="el" href="classshark_1_1_regularized_kernel_matrix.html#a5b05f90e9cab384c7a217a6d3e16550b">shark::RegularizedKernelMatrix&lt; InputType, CacheType &gt;</a></li>
<li>resetDifferences()&#160;:&#160;<a class="el" href="classshark_1_1_energy_storing_tempered_markov_chain.html#a2b1b396adf72f4143d7a22bf969bb114">shark::EnergyStoringTemperedMarkovChain&lt; Operator &gt;</a></li>
<li>reshrink()&#160;:&#160;<a class="el" href="classshark_1_1_box_constrained_problem.html#a379f06bbd07f0f7851596b5a43f53d3d">shark::BoxConstrainedProblem&lt; SVMProblem &gt;</a>, <a class="el" href="classshark_1_1_svm_problem.html#a073a4ac3cd7dddbd840fe8b38ca2b399">shark::SvmProblem&lt; Problem &gt;</a></li>
<li>resize()&#160;:&#160;<a class="el" href="structshark_1_1_batch_3_01shark_1_1blas_1_1compressed__vector_3_01_t_01_4_01_4.html#a818fad0d1a5ebeee0dd3d86c98bb21e0">shark::Batch&lt; shark::blas::compressed_vector&lt; T &gt; &gt;</a>, <a class="el" href="classshark_1_1_binary_layer.html#a443969d7e0912b0d213ca4b31de8f4aa">shark::BinaryLayer</a>, <a class="el" href="classshark_1_1_bipolar_layer.html#a29da721e3d84201b8620fb8d3175b402">shark::BipolarLayer</a>, <a class="el" href="classshark_1_1_gaussian_layer.html#ad0d61deb12bd4455dbf6546d08328b72">shark::GaussianLayer</a>, <a class="el" href="classshark_1_1_multi_variate_normal_distribution.html#a58c4c416f7e516ccd28dc53ae7828d78">shark::MultiVariateNormalDistribution</a>, <a class="el" href="classshark_1_1_multi_variate_normal_distribution_cholesky.html#a3282df396618643594c4e4e5ea932862">shark::MultiVariateNormalDistributionCholesky</a>, <a class="el" href="structshark_1_1_normalizer_neuron_1_1_state.html#a7f6662070f8f4d6392645eb81388e9ac">shark::NormalizerNeuron&lt; VectorType &gt;::State</a>, <a class="el" href="classshark_1_1_qp_sparse_array.html#a4b337a24b7e0345a681d912b917f16ff">shark::QpSparseArray&lt; QpFloatType &gt;</a></li>
<li>ResizeLayer()&#160;:&#160;<a class="el" href="classshark_1_1_resize_layer.html#a05beadeb7f4aec71d513d624b9ca95be">shark::ResizeLayer&lt; VectorType &gt;</a></li>
<li>resizeLine()&#160;:&#160;<a class="el" href="classshark_1_1_l_r_u_cache.html#a17d6a25d7e772893c10ec0cdd3f8670f">shark::LRUCache&lt; T &gt;</a></li>
<li>restoreOriginalLabels()&#160;:&#160;<a class="el" href="classshark_1_1_label_order.html#ac3a45a99342f3d066d85bc8b2bbbe86b">shark::LabelOrder</a></li>
<li>result&#160;:&#160;<a class="el" href="classshark_1_1_evaluation_archive_1_1_point_result_pair_type.html#a82041e6a8488ae6d6250c6a71105f556">shark::EvaluationArchive&lt; PointType, ResultT &gt;::PointResultPairType</a></li>
<li>ResultSet&#160;:&#160;<a class="el" href="classshark_1_1_abstract_stopping_criterion.html#aa813314942f2e999ac8b9c985d001f96">shark::AbstractStoppingCriterion&lt; ResultSetT &gt;</a>, <a class="el" href="classshark_1_1_generalization_loss.html#acbef2a032c71c0a28a69b74923dfe39d">shark::GeneralizationLoss&lt; PointType &gt;</a>, <a class="el" href="classshark_1_1_generalization_quotient.html#a374f20c6f4a96f98879266b5d4798d1e">shark::GeneralizationQuotient&lt; PointType &gt;</a>, <a class="el" href="structshark_1_1_result_set.html#abcbaa0cc10e7ecd9514cedabc09b44b5">shark::ResultSet&lt; SearchPointT, ResultT &gt;</a>, <a class="el" href="classshark_1_1_training_progress.html#a313c1aa65f6489d735fce017d01cfc94">shark::TrainingProgress&lt; PointType &gt;</a></li>
<li>ResultTable()&#160;:&#160;<a class="el" href="classshark_1_1statistics_1_1_result_table.html#a55b1f8bc5a3768c332a6812adf52111c">shark::statistics::ResultTable&lt; Parameter &gt;</a></li>
<li>ResultType&#160;:&#160;<a class="el" href="classshark_1_1_abstract_objective_function.html#a70f0672a3c3b24c437c81243624b5307">shark::AbstractObjectiveFunction&lt; PointType, ResultT &gt;</a>, <a class="el" href="classshark_1_1_abstract_optimizer.html#a89ed73f010deb3809acbcf23160c0f6b">shark::AbstractOptimizer&lt; PointType, ResultT, SolutionTypeT &gt;</a>, <a class="el" href="classshark_1_1_abstract_single_objective_optimizer.html#a068a68c8739215f4a13ce8a433ec38b3">shark::AbstractSingleObjectiveOptimizer&lt; PointType &gt;</a>, <a class="el" href="classshark_1_1_error_function.html#a3c50a0db21038a95f674d5f173806a8a">shark::ErrorFunction&lt; SearchPointType &gt;</a>, <a class="el" href="classshark_1_1_evaluation_archive.html#ae13e5ea958abc67e9e369dce00a92b8c">shark::EvaluationArchive&lt; PointType, ResultT &gt;</a>, <a class="el" href="structshark_1_1_result_set.html#a6643e0c7e17f497f9158a3a4f59753f8">shark::ResultSet&lt; SearchPointT, ResultT &gt;</a>, <a class="el" href="structshark_1_1_single_objective_result_set.html#a2724afa837b424895c634d11ca899bce">shark::SingleObjectiveResultSet&lt; SearchPointTypeT &gt;</a></li>
<li>RFTrainer()&#160;:&#160;<a class="el" href="classshark_1_1_r_f_trainer_3_01_real_vector_01_4.html#aa70d6bf3520d6a5961cdacfa7f540d36">shark::RFTrainer&lt; RealVector &gt;</a>, <a class="el" href="classshark_1_1_r_f_trainer_3_01unsigned_01int_01_4.html#a53204a1b23c9d4faa542ce5179162b9f">shark::RFTrainer&lt; unsigned int &gt;</a></li>
<li>right()&#160;:&#160;<a class="el" href="classshark_1_1_binary_tree.html#ae5167564abb964d90da01897693a5768">shark::BinaryTree&lt; InputT &gt;</a></li>
<li>rightIdOrIndex&#160;:&#160;<a class="el" href="structshark_1_1_c_a_r_tree_1_1_node.html#a76b5e6f60f3f6a8d5938c5ac4ccb1a4b">shark::CARTree&lt; LabelType &gt;::Node</a></li>
<li>rng()&#160;:&#160;<a class="el" href="classshark_1_1_r_b_m.html#a7ec198cf576079447b2c78661625980b">shark::RBM&lt; VisibleLayerT, HiddenLayerT, randomT &gt;</a></li>
<li>ROC()&#160;:&#160;<a class="el" href="classshark_1_1_r_o_c.html#a86d4d7fe5ba1f81b0fd212127d7679cd">shark::ROC</a></li>
<li>Rosenbrock()&#160;:&#160;<a class="el" href="structshark_1_1benchmarks_1_1_rosenbrock.html#a253269afd1fde83d8015f0d837b2f8a6">shark::benchmarks::Rosenbrock</a></li>
<li>RotatedObjectiveFunction()&#160;:&#160;<a class="el" href="structshark_1_1benchmarks_1_1_rotated_objective_function.html#abc34880a75d766d9ec8e2cd835ee3a8d">shark::benchmarks::RotatedObjectiveFunction</a></li>
<li>row()&#160;:&#160;<a class="el" href="classshark_1_1_block_matrix2x2.html#a1651f0def46f95b6d1d502c409b2b187">shark::BlockMatrix2x2&lt; Matrix &gt;</a>, <a class="el" href="classshark_1_1_cached_matrix.html#a09afc06315ddf58b64497d67886cb805">shark::CachedMatrix&lt; Matrix &gt;</a>, <a class="el" href="classshark_1_1_difference_kernel_matrix.html#aac4a479d840e95943b5bcd1a79f4d24b">shark::DifferenceKernelMatrix&lt; InputType, CacheType &gt;</a>, <a class="el" href="classshark_1_1_example_modified_kernel_matrix.html#aad7d35cd53538110b06c0aa455b1e05d">shark::ExampleModifiedKernelMatrix&lt; InputType, CacheType &gt;</a>, <a class="el" href="classshark_1_1_gaussian_kernel_matrix.html#aac169d6b44199778c9a15f3434fc0a09">shark::GaussianKernelMatrix&lt; T, CacheType &gt;</a>, <a class="el" href="classshark_1_1_kernel_matrix.html#a3ebcd91ea57f440376d14741e7697c6d">shark::KernelMatrix&lt; InputType, CacheType &gt;</a>, <a class="el" href="classshark_1_1_modified_kernel_matrix.html#a4c911a252144076bd39e5aad62e37475">shark::ModifiedKernelMatrix&lt; InputType, CacheType &gt;</a>, <a class="el" href="classshark_1_1_partly_precomputed_matrix.html#aed8139929435c448eac748cf119ef275">shark::PartlyPrecomputedMatrix&lt; Matrix &gt;</a>, <a class="el" href="classshark_1_1_precomputed_matrix.html#a23d4a870760436710e017f9df9bf4e05">shark::PrecomputedMatrix&lt; Matrix &gt;</a>, <a class="el" href="classshark_1_1_qp_sparse_array.html#a48374ef20f984fd3d290600fc5af63f8">shark::QpSparseArray&lt; QpFloatType &gt;</a>, <a class="el" href="classshark_1_1_regularized_kernel_matrix.html#a4c6ca54b0374328f55954e902b88321d">shark::RegularizedKernelMatrix&lt; InputType, CacheType &gt;</a></li>
<li>Rprop()&#160;:&#160;<a class="el" href="classshark_1_1_rprop.html#a390d96027842e4f13ff3a1a32853951b">shark::Rprop&lt; SearchPointType &gt;</a></li>
<li>RVEA()&#160;:&#160;<a class="el" href="classshark_1_1_r_v_e_a.html#aea4a153e99eb52eb6e946d5b1da4e275">shark::RVEA</a></li>
</ul>
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